@Article{cmc.2021.016816,
AUTHOR = {Abdul Majid, Muhammad Attique Khan, Yunyoung Nam, Usman Tariq, Sudipta Roy, Reham R. Mostafa, Rasha H. Sakr},
TITLE = {COVID19 Classification Using CT Images via Ensembles of Deep Learning Models},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {69},
YEAR = {2021},
NUMBER = {1},
PAGES = {319--337},
URL = {http://www.techscience.com/cmc/v69n1/42737},
ISSN = {1546-2226},
ABSTRACT = {The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the choice of key features. Here, we propose a set of deep learning features based on a system for automated classification of computed tomography (CT) images to identify COVID-19. Initially, this method was used to prepare a database of three classes: Pneumonia, COVID-19, and Healthy. The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach. In the next step, two advanced deep learning models (ResNet50 and DarkNet53) were fine-tuned and trained through transfer learning. The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach. For each deep model, the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach. Later, the selected features were merged using the new minimum parallel distance non-redundant (PMDNR) approach. The final fused vector was finally classified using the extreme machine classifier. The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%. Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.},
DOI = {10.32604/cmc.2021.016816}
}